One year healthcare consumption prior to sudden cardiac death
Bibliographic record
Abstract
AIMS: Identifying individuals at high risk of sudden cardiac death (SCD) is key, and knowledge on health care consumption prior to the event may be relevant to address this challenge. This study aimed to evaluate healthcare consumption patterns in the year preceding SCD. METHODS AND RESULTS: SCD cases were collected from the Paris Sudden Death Expertise Centre (SDEC) from 2011 to 2020. Using electronic health records from the French National Health Insurance System, all medical interactions were analysed in the year prior to SCD occurrence. To contextualize our findings, the SDEC population was compared with national-level data from the French population, categorized as either above or below the population average, across 2 dimensions: number of hospital diagnoses (primary diagnosis or emergency room visit) and outpatient visits (general practitioner or cardiologist visit). 21 912 SCD were included in the study. Compared with the general population, three distinct patterns of healthcare consumption were identified. The low-interaction group (14%) had minimal healthcare contact, with 3.8 times fewer outpatient and 13.5 times fewer inpatient visits. The intermediate group (40%) showed modest engagement, recording 1.5 times fewer outpatient visits and 5.8 times more inpatient visits. The high-interaction group (46%) had 2.7 times more outpatient and 7.9 times more inpatient visits compared with the general population. CONCLUSION: This study highlights significant variability in healthcare use during the year preceding SCD. Nearly half of patients had frequent healthcare contacts, suggesting opportunities for earlier identification and prevention.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".